边缘中心脑Transformer：基于fMRI的脑疾病诊断的边缘中心功能连接学习框架
Edge-centric Brain Transformer: An Edge-centric Functional Connectivity Learning Framework for fMRI-based Brain Disorder Diagnosis
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- School of Mathematics and Statistics, Shandong University（山东大学数学与统计学院）
- Academy of Mathematics and Systems Science, University of Chinese Academy of Sciences（中国科学院数学与系统科学研究院）
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中文总结 AI 辅助
提出边缘中心脑Transformer（EBT）框架，将rs-fMRI分析重构为功能连接表示学习，通过边缘时间序列和线图建模连接动态，利用结构感知Transformer和边缘级聚类读出模块，在多个数据集上优于现有方法，并发现与病理一致的疾病相关连接生物标志物。
中文摘要 AI 辅助
静息态功能磁共振成像（rs-fMRI）能够表征分布式脑区域之间的功能交互，并已在脑疾病诊断中展现出潜力。然而，现有的深度学习方法主要依赖节点中心表示，其中脑区域作为主要学习单元，可能忽略功能连接中嵌入的判别性改变。在此，我们提出一种边缘中心脑Transformer（EBT）框架，将rs-fMRI分析重新表述为功能连接表示学习。EBT不是独立建模脑区域，而是构建边缘时间序列表示以捕获功能连接的动态共波动模式，并将判别性连接组织成线图以进行显式的连接间建模。开发了一种结构感知Transformer，以学习解剖相关连接之间的局部依赖性和跨分布式功能网络的全局交互。此外，引入边缘级正交聚类读出模块，以推导受试者级表示并识别与脑疾病相关的潜在连接模块。在多个神经影像数据集上的评估表明，EBT始终优于代表性的图神经网络、脑Transformer和传统基于连接的方法。可解释性分析进一步揭示了与已知病理网络改变一致的稳定疾病相关功能连接和连接模块。这些发现为基于rs-fMRI的脑疾病诊断建立了边缘中心视角，并为发现可解释的连接生物标志物提供了有前景的框架。源代码公开于：此HTTPS URL。
英文摘要
Resting-state functional magnetic resonance imaging (rs-fMRI) enables the characterization of functional interactions among distributed brain regions and has shown promise for brain disorder diagnosis. However, existing deep learning methods predominantly rely on node-centric representations, where brain regions serve as the primary learning units, potentially overlooking discriminative alterations embedded in functional connections. Here, we propose an edge-centric brain transformer (EBT) framework that reformulates rs-fMRI analysis as functional connection representation learning. Instead of modeling brain regions independently, EBT constructs edge time-series representations to capture dynamic co-fluctuation patterns of functional connections and organizes discriminative connections into a line graph for explicit connection-to-connection modeling. A structure-aware transformer is developed to learn both local dependencies among anatomically related connections and global interactions across distributed functional networks. Furthermore, an edge-level orthogonal clustering readout module is introduced to derive subject-level representations and identify latent connectivity modules associated with brain disorders. Evaluations on multiple neuroimaging datasets demonstrate that EBT consistently outperforms representative graph neural networks, brain transformers, and conventional connectivity-based approaches. Interpretability analyses further reveal stable disease-associated functional connections and connectivity modules that align with known pathological network alterations. These findings establish an edge-centric perspective for rs-fMRI-based brain disorder diagnosis and provide a promising framework for discovering interpretable connectivity biomarkers. The source code is publicly available at: https://github.com/Zdy12/Edge-centric-Brain-Transformer.
